Machine-Learning-Based Prediction of Corrosion Behavior in Additively Manufactured Inconel 718

O. V. Mythreyi, M. Rohith Srinivaas, Tigga Amit Kumar and R. Jayaganthan
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O. V. Mythreyi: Department of Engineering Design, Indian Institute of Technology Madras, Chennai 600036, India
M. Rohith Srinivaas: Department of Metallurgical & Materials Engineering, Indian Institute of Technology Madras, Chennai 600036, India
Tigga Amit Kumar: Gas Turbine Research Establishment Research and Development Organization, Bengaluru 560093, India
R. Jayaganthan: Department of Engineering Design, Indian Institute of Technology Madras, Chennai 600036, India

Data, 2021, vol. 6, issue 8, 1-16

Abstract: This research work focuses on machine-learning-assisted prediction of the corrosion behavior of laser-powder-bed-fused (LPBF) and postprocessed Inconel 718. Corrosion testing data of these specimens were collected and fit into the following machine learning algorithms: polynomial regression, support vector regression, decision tree, and extreme gradient boosting. The model performance, after hyperparameter optimization, was evaluated using a set of established metrics: R 2 , mean absolute error, and root mean square error. Among the algorithms, the extreme gradient boosting algorithm performed best in predicting the corrosion behavior, closely followed by other algorithms. Feature importance analysis was executed in order to determine the postprocessing parameters that influenced the most the corrosion behavior in Inconel 718 manufactured by LPBF.

Keywords: selective laser melting; Inconel 718; machine learning; corrosion prediction; extreme gradient boosting (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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